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The Impact of Embodied Intelligence and AI Leasing on the Commercialization Process of Humanoid Robots

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DOI: 10.23977/jaip.2025.080311 | Downloads: 0 | Views: 32

Author(s)

Xingran Chen 1

Affiliation(s)

1 Xiamen No.2 Middle School of Fujian, Xiamen, Fujian, 361002, China

Corresponding Author

Xingran Chen

ABSTRACT

This study examines the impact of embodied intelligence, AI leasing, and data assetization on the commercialization of humanoid robots through three dimensions. Key findings reveal: Technologically, embodied intelligence enhances dynamic task success rates and reduces hardware costs by optimizing motion control algorithms (e.g., hierarchical reinforcement learning) and perception-cognition fusion architectures (VLA models), surpassing industrial usability thresholds. Economically, AI leasing restructures cost models via dual-track computility/robot leasing, converting CapEx to OpEx to achieve per-unit costs significantly below human labor. This triggers economies of scale in manufacturing (MIT-validated: adoption surges when leasing costs fall below 70% of human labor expenses). Ecologically, data assetization bridges training gaps with simulation data, activates capital cycles through financialization, and lowers R&D barriers via standardization (e.g., improved training efficiency on heterogeneous datasets), establishing collaborative industrial foundations. These forces form a self-reinforcing cycle: technological cost reduction → leasing-driven scaling → data-enabled iteration, accelerating global market growth. Future competition will center on federated learning privacy frameworks and physical agent protocol dominance. Chinese enterprises must secure rule-making power through policy-technology-capital tripartite synergy.

KEYWORDS

Embodied Intelligence, AI Leasing, Data Assetization, Humanoid Robots

CITE THIS PAPER

Xingran Chen, The Impact of Embodied Intelligence and AI Leasing on the Commercialization Process of Humanoid Robots. Journal of Artificial Intelligence Practice (2025) Vol. 8: 77-89. DOI: http://dx.doi.org/10.23977/jaip.2025.080311.

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